Initial commit: obsidian to gitea

phd/weekly-report/260118.md

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+Objectives
+- Auto LLM inference config tuner
+
+Key Results
+- [6/10] Build the agentic tuner system
+- [10/10] Build the first version auto tuner system
+- [2/10] Workload grouping methods
+- [8/10] Check the current situation of parallelism config optimization
+- [4/10] Understand the possibility/challenges in LLM inference compute graph arrangement automatically
+- [1/10] Define the IR for automatic optimization
+- [5/10] Profile different parallelism setup with real trace and analysis their difference
+
+Last Week
+- [KR1] Update agentic AITuner to support new trace benchmark / new vLLM flags/ objective score. [0a012bdd](https://ipads.se.sjtu.edu.cn:1312/wangjh/auto-tuner/-/commit/0a012bdda53086cd24277962abb0cb559bd313bb) ~ [788da3d8](https://ipads.se.sjtu.edu.cn:1312/wangjh/auto-tuner/-/commit/788da3d8bc546620e8c76800dfb7070372cb3540)
+- [KR1] Survey the related works. Some works build an agent for LLM training / storage system / ..., a work use BO for LLM inference config tuning.
+- [misc] Prepare an open-sourced version of new traces (thinking and coder) and update readme.
+
+Next Week
+- Optimize the agentic AITuner.
+- Test SCOOT as one of the baseline.